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score_matrix_t | score_matrix {} |
| | The scoring matrix.
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trace_matrix_t | trace_matrix {} |
| | The trace matrix if needed.
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score_matrix_t::iterator | score_matrix_iter {} |
| | The matrix iterator over the score matrix.
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trace_matrix_t::iterator | trace_matrix_iter {} |
| | The matrix iterator over the trace matrix.
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constexpr | alignment_matrix_policy ()=default |
| | Defaulted.
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constexpr | alignment_matrix_policy (alignment_matrix_policy const &)=default |
| | Defaulted.
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constexpr | alignment_matrix_policy (alignment_matrix_policy &&)=default |
| | Defaulted.
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constexpr alignment_matrix_policy & | operator= (alignment_matrix_policy const &)=default |
| | Defaulted.
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constexpr alignment_matrix_policy & | operator= (alignment_matrix_policy &&)=default |
| | Defaulted.
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| ~alignment_matrix_policy ()=default |
| | Defaulted.
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template<typename configuration_t > |
| | alignment_matrix_policy (configuration_t const &) |
| | Initialise the policy.
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| template<typename sequence1_t , typename sequence2_t > |
| constexpr void | allocate_matrix (sequence1_t &&sequence1, sequence2_t &&sequence2) |
| | }
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| template<typename sequence1_t , typename sequence2_t , typename score_t > |
| constexpr void | allocate_matrix (sequence1_t &&sequence1, sequence2_t &&sequence2, align_cfg::band_fixed_size const &band, alignment_algorithm_state< score_t > const &state) |
| | Allocates the memory of the underlying matrices.
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constexpr void | initialise_matrix_iterator () noexcept |
| | Initialises the score and trace matrix iterator after allocating the matrices.
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| template<typename sequence1_t , typename sequence2_t > |
| constexpr auto | slice_sequences (sequence1_t &sequence1, sequence2_t &sequence2, align_cfg::band_fixed_size const &band) const noexcept |
| | Slices the sequences according to the band parameters.
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| constexpr auto | current_alignment_column () noexcept |
| | Returns the current alignment column.
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| constexpr void | next_alignment_column () noexcept |
| | Moves to the next alignment column.
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template<typename
alignment_algorithm_t, typename score_matrix_t, typename trace_matrix_t>
class seqan3::detail::alignment_matrix_policy< alignment_algorithm_t, score_matrix_t, trace_matrix_t >
Manages the alignment and score matrix.
- Template Parameters
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| alignment_algorithm_t | The derived type (seqan3::detail::alignment_algorithm) to be augmented with this CRTP-policy. |
| score_matrix_t | The type of the alignment score matrix. |
| trace_matrix_t | The type of the alignment trace matrix. |
This policy is used to manage the score and trace matrix of the alignment algorithm. On invocation of an alignment instance the necessary memory is allocated and the corresponding matrix iterators are initialised. These iterators are used as a global state within this particular alignment instance and are accessed from the alignment algorithm.
template<typename sequence1_t , typename sequence2_t , typename score_t >
Allocates the memory of the underlying matrices.
- Template Parameters
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| sequence1_t | The type of the first sequence to align; must model std::forward_ranges. |
| sequence2_t | The type of the second sequence to align; must model std::forward_ranges. |
| score_t | The score type used inside of the alignment algorithm. |
- Parameters
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| [in] | sequence1 | The first sequence to align. |
| [in] | sequence2 | The second sequence to align. |
| [in] | band | The band used to initialise the matrices. |
| [in] | state | The current state used by the alignment algorithm. |
Initialises the underlying banded score and trace matrices and sets the respective matrix iterators to the begin of the corresponding matrix. Using the additional band parameter the actual dimensions are reduced according to the matrix implementation. For the banded case, one additional cell per column is stored such that we can read from it without introducing a case distinction inside of the algorithm implementation. However, this cell needs to be properly initialised with an infinity value. To emulate the infinity for integral values we use the smallest representable value and subtract the gap extension score (assumed to be always negative) from it. In the algorithm we never write to this cell and only add the extension costs to the read value. This way we can get the smallest possible value as an infinity.
1.9.8